Agrophotovoltaic (APV) system is an integrated system producing crops as well as solar energy. Because crop production underneath Photovoltaic (PV) modules requires delicate management of crops, smart farming equipment such as real-time remote monitoring sensors (e.g., soil moisture sensors) and micro-climate monitoring sensors (e.g., thermometers and irradiance sensors) is installed in the APV system. This study aims at introducing a decision support system (DSS) for smart farming in an APV system. The proposed DSS is devised to provide a mobile application service, satellite image processing, real-time data monitoring, and performance estimation. Particularly, the real-time monitoring data is used as an input of the DSS system for performance estimation of an APV system in terms of production yields of crops and monetary benefit so that a data-driven function is implemented in the proposed system. The proposed DSS is validated with field data collected from an actual APV system at the Jeollanamdo Agricultural Research and Extension Services in South Korea. As a result, farmers and engineers enable to efficiently produce solar energy without causing harmful impact on regular crop production underneath PV modules. In addition, the proposed system will contribute to enhancement of the smart farming technology in the field of agriculture.

Agrophotovoltaic (APV) system is an integrated system producing crops as well as solar energy. Because crop production underneath Photovoltaic (PV) modules requires delicate management of crops, smart farming equipment such as real-time remote monitoring sensors (e.g., thermometers, irradiance sensors, and soil moisture sensors) is installed in the APV system. This study aims at introducing a simulation-based decision support system (DSS) for smart farming in an APV system. The proposed DSS is devised to provide a mobile application service, satellite image processing, real-time data monitoring, and simulation-based performance estimation. Particularly, an agent-based simulation (ABS) is used to mimic functions of an APV system so that a data-driven function and digital twin environment are implemented in the proposed system. The ABS model is validated with field data collected from an actual APV system at the Jeollanamdo Agricultural Research and Extension Services in South Korea. As a result, farmers and engineers enable to efficiently produce solar energy without causing harmful impact on regular crop production underneath PV modules. In addition, the proposed system will contribute to enhancement of the digital twin technology in the field of agriculture.

In this paper, we propose a system architecture of the AMI to be applied in the modern agricultural sector. Agricultural electricity costs in South Korea is very inexpensive compared with other industries. It is expected to increase oil prices to rise over the medium to long term so the facilities must to be installed for farmers in terms of energy savings and energy costs. The research and development of plant factory which can replace the ills of modern agriculture is very active. The technologies of smart grid and plat factory are good paradigm of next generation agricultural sector. Good use of smart grid technologies, the traditional energy consumption industries, agriculture sector can be self-sufficiency industry. In this article the AMI architecture is developed and it will be applicable for modern farmers plant factory.

The purpose of this study was to examine the intention of consumer acceptance of technology in agricultural production by applying the unified theory of acceptance and use of technology (UTAUT) to smart farm. In particular, this study analyzed the intention to accept the technology of agricultural students, farmers, start-up farmers, returning farmers, and returnees in the general manufacturing industry and high-tech industries, and in agricultural sectors corresponding to primary industries. The results showed that performance expectancy, social influence, facilitating conditions, IT development level, and reliability had a significant influence on the intention to use smart farm technology. However, effort expectancy and price value were rejected because no significant impact on use intention was tested. In addition, the influences of the variables showing their influence were reliability (β=.569) > IT development level (β=.252) > social influence (β=.235) > performance expectancy (β=.182) > facilitating conditions (β=.134).

인구감소, 고령화, 이상기후 등의 극복을 위해 기존의 노동집약적 방식에서 자본집약적 방식의 첨단산업화로 영농환경은 변화하고 있다. 영농환경의 변화는 미래 시장의 기회로 삼을 수 있으며 다양한 ICT(Information and Communication Technologies) 융복합의 스마트 팜 등이 영농의 계량화, 자동화, 네트워크화로 실현되고 있다. 시설원예·과수·축산 분야에 센서 등 다양한 ICT영농기기와 복합자동환경제어시스템을 도입한 혁신농장 스마트 팜은 온·습도 등을 원격 모니터링하고, 축적된 생육정보를 활용하여 최적상태로 생장을 관리할 수 있는 첨단 시스템이다. 스마트 팜 농업은 생산성 증진·품질 향상 뿐만 아니라, 생산·유통·소비과정에서 비용절감 효과가 크다는 장점이 있다. 오늘날과 같은 경쟁 속에서 경쟁력 확보를 위 한 농식품 포장디자인의 중요성은 날로 증대되고 있고 농업의 생산구조가 변화하는 시점에서 농산물 포장디자인도 변화되 어야 할 것이다. 정보통신 기술을 농업에 접목한 스마트 팜 농업을 위한 포장디자인 연구가 절실히 필요하며 그 중 딸기 생산을 중심으로 한 실제사례 발표를 통해 변화하는 농업에 기여하고자 한다.